🕶 Happy Data are all Alike

What graphs will we see today?

Some of the very basic and commonly used plots for data are: - Bar and Column Charts - Histograms and Frequency Distributions - Scatter Plots (if there is more than one quant variable) and - 2D Hexbins Plots and 2D Frequency Distributions (horrors?)

An Example: Bar and Column Charts

Here is a dataset from Jeremy Singer-Vine’s blog, Data Is Plural. This is a list of all books banned in schools across the US.

## # A tibble: 1,586 × 10
##    author  title type_of_ban secondary_author_s illustrator_s translator_s state
##    <chr>   <chr> <chr>       <chr>              <chr>         <chr>        <chr>
##  1 Àbíké-… Ace … Banned fro… <NA>               <NA>          <NA>         Flor…
##  2 Aceved… Clap… Banned fro… <NA>               <NA>          <NA>         Penn…
##  3 Aceved… The … Banned fro… <NA>               <NA>          <NA>         Flor…
##  4 Aceved… The … Banned fro… <NA>               <NA>          <NA>         New …
##  5 Aceved… The … Banned Pen… <NA>               <NA>          <NA>         Texas
##  6 Aceved… The … Banned Pen… <NA>               <NA>          <NA>         Virg…
##  7 Aciman… Call… Banned Pen… <NA>               <NA>          <NA>         Virg…
##  8 Acito,… How … Banned Pen… <NA>               <NA>          <NA>         Flor…
##  9 Adeyoh… 47,0… Banned fro… Adeyoha, Angel     McGillis, Ho… <NA>         Penn…
## 10 Adichi… Half… Banned fro… <NA>               <NA>          <NA>         Mich…
## # ℹ 1,576 more rows
## # ℹ 3 more variables: district <chr>, date_of_challenge_removal <chr>,
## #   origin_of_challenge <chr>

Banned Books data

banned_by_state <- banned %>% 
  group_by(state) %>% 
  summarise(total = n())
banned_by_state

## # A tibble: 26 × 2
##    state    total
##    <chr>    <int>
##  1 Alaska       1
##  2 Arkansas     1
##  3 Florida    204
##  4 Georgia     13
##  5 Illinois     4
##  6 Indiana     18
##  7 Iowa         4
##  8 Kansas      30
##  9 Maryland     1
## 10 Michigan     2
## # ℹ 16 more rows

banned %>% 
  group_by(state, type_of_ban) %>% 
  summarise(count = n()) %>%
  slice_max(order_by = count,n = 10) %>%
 #  pivot_wider(.,id_cols = State,
 #              names_from = `Type of Ban`,
 #              values_from = count) %>% janitor::clean_names() %>% 
 #  replace_na(list(banned_from_libraries_and_classrooms = 0,
 #                  banned_from_libraries = 0,
 #                  banned_pending_investigation = 0,
 #                  banned_from_classrooms = 0)) %>% 
 # mutate(total = sum(across(where(is.integer)))) %>%
ggplot(aes(x = reorder(state, count), y = count, fill = type_of_ban)) + geom_col() + labs(title = "Banned Books by Type across the US") + xlab("State") + coord_flip() + theme_classic()

## `summarise()` has grouped output by 'state'. You can override using the
## `.groups` argument.

An Example: Histograms and Frequency Distributions

TBD: Example using Flourish

An Example: Frequency Density

Your Turn

1. Rbnb Price Data on the French Riviera:

Rbnb data

1. Apartment price vs ground living area:

Apartment Data

(Try a Scatter Plot too, since we have two Quant variables)

1. Rbnb Price Data on the French Riviera:

Rbnb data

1. India
2. Old Faithful Data
3. Income data
4. Diamonds Data from R
5. calmcode.io dataset

Fun Stuff

1. See the scrolly animation for a histogram at this website: Exploring Histograms, an essay by Aran Lunzer and Amelia McNamara https://tinlizzie.org/histograms/?s=09